This project is a continuation of our ongoing work on the physiology of the agitation-depression reaction seen in unrestrained group-living infant monkeys separated from their mothers. The depressive component of this reaction is thought to be one to the best available animal models of human depression, and perhaps of certain psychophysiological disorders as well. Our objectives in this project are five in number: 1. we will further define long-term physiological alterations that result from the stress of maternal separation and will determine what aspects of the early mother-infant relationship, or early physiological development, may permit the prediction of such long-term changes, 2. we will define, from a longitudinal viewpoint, normal physiological development, and the organization of behavioral/physiological correlations, during the first year of life in the monkey infant (as a model of human development), 3. we will study the physiological and behavioral development of infants raised in a variety of altered attachment relationships, including isolation rearing and surrogate rearing, and will investigate the physiological and behavioral consequences of the disruption of these atypical attachment bonds, 4. we will begin the investigation of pharmacological mechanisms underlying the agitation-depression reaction, and 5. we will study the electrophysiological activity patterns (including multiple unit activity), of various limbic structures during the agitation-depression reaction, as a necessary first step in defining underlying neurophysiological mechanisms. All infants will be studied completely unrestrained, with physiological data obtained by means of totally implantable multichannel biotelemetry systems of our design that transmit EKG, body temperature, EOG, EMG, and channels of EEG. All physiological data will be recorded and processed in real time using online minicomputer-based experiment controller system designed for this purpose. Simultaneous behavioral data will also be recorded and processed in real time using an online computer voice recognition system.